Temporal fruit microbiome and immunity dynamics in postharvest apple (Malus x domestica)

Roselane Kithan-Lundquist , Hannah M. McMillan , Sheng-Yang He , George W. Sundin

Horticulture Research ›› 2025, Vol. 12 ›› Issue (6) : 63

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Horticulture Research ›› 2025, Vol. 12 ›› Issue (6) :63 DOI: 10.1093/hr/uhaf063
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Temporal fruit microbiome and immunity dynamics in postharvest apple (Malus x domestica)

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Abstract

The plant immune response plays a central role in maintaining a well-balanced and healthy microbiome for plant health. However, insights into how the fruit immune response and the fruit microbiome influence fruit health after harvest are limited. We investigated the temporal dynamics of the fruit microbiota and host defense gene expression patterns during postharvest storage of apple fruits at room temperature. Our results demonstrate a temporal dynamic shift in both bacterial and fungal community composition during postharvest storage that coincides with a steep-decline in host defense response gene expression associated with pattern-triggered immunity. We observed the gradual appearance of putative pathogenic/spoilage microbes belonging to genera Alternaria (fungi) and Gluconobacter and Acetobacter (bacteria) at the expense of Sporobolomyces and other genera, which have been suggested to be beneficial for plant hosts. Moreover, artificial induction of pattern-triggered immunity in apple fruit with the flg22 peptide delayed the onset of fruit rot caused by the fungal pathogen Penicillium expansum. Our results suggest that the fruit immune response helps to orchestrate a microbiome and that the collapse of the immunity results in the proliferation of spoilage microbes and fruit rot. These findings hold implications for the development of strategies to increase fruit quality and prolong shelf life in fruits and vegetables.

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Roselane Kithan-Lundquist, Hannah M. McMillan, Sheng-Yang He, George W. Sundin. Temporal fruit microbiome and immunity dynamics in postharvest apple (Malus x domestica). Horticulture Research, 2025, 12(6): 63 DOI:10.1093/hr/uhaf063

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Acknowledgments

Funding support was provided by Michigan State University AgBioResearch and the Michigan Apple Committee. We thank Cory Outwater for assistance in orchard maintenance and Dr. Tyre Proffer with his assistance in culturing and inoculation of P. expansum. We also want to thank Dr. Kristi MacCready, Dr. Xiaochen Yuan, Dr. Karla Vasco, and Julian Liber for their help in training and troubleshooting problems with data analysis. We thank Jerimiah Johnson, Kirsten Pollok, Ishani Misra, Noah Diaz, Kevin Mclaughlin, and Elise Straub for their help with sample processing. Hannah McMillan is supported by a Postdoctoral Fellowship in Biology from the National Science Foundation (Award Number 2208939). Sheng Yang He is an investigator at Howard Hughes Medical Institute. Roselane Kithan-Lundquist was supported by a fellowship from the US National Institutes of Health Plant Biotechnology for Health and Sustainability Training Program at Michigan State University (Grant nos: NIH T32-GM110523).

Author contributions

GS, SYH, and RK-L conceptualized and designed the experiment. RK-L performed the experiments and analyzed the data. HM contributed to data analysis and interpretation of the results. RK-L wrote the first draft. HM, SYH, and GS provided critical comments and edited the manuscript.

Data availability

Raw data used in this study are available in the NCBI Sequence Read Archive (SRA) under the BioProject IDs: PRJNA1112867 and PRJNA1112895.

Conflict of interest statement

None declared.

Supplementary data

Supplementary data is available at Horticulture Research online.

References

[1]

Berg G, Rybakova D, Fischer D. et al. Microbiome definition re-visited: old concepts and new challenges. Microbiome. 2020; 8:103

[2]

Cordovez V, Dini-andreote F, Carrion VJ. et al. Ecology and evo-lution of plant microbiomes. Ann Rev Microbiol. 2019; 73:69-88

[3]

Turner TR, James EK, Poole PS. The plant microbiome. Genome Biol. 2013; 14:209

[4]

Wassermann B, Kusstatscher P, Berg G. Microbiome response to hot water treatment and potential synergy with biological control on stored apples. Front Microbiol. 2019a; 10:2502

[5]

Berendsen RL, Pieterse CM, Bakker PA. The rhizosphere micro-biome and plant health. Trends Plant Sci. 2012; 17:478-86

[6]

Sohrabi R, Paasch BC, Liber JA. et al. Phyllosphere microbiome. Annu Rev Plant Biol. 2023; 74:539-68

[7]

Niu B, Paulson JN, Zheng X. et al. Simplified and representative bacterial community of maize roots. Proc Natl Acad Sci USA. 2017;114:E2450-9

[8]

Berg G, Rybakova D, Grube M. et al. Europe PMC funders group the plant microbiome explored: implications for experimental botany. JExp Bot. 2017; 67:995-1002

[9]

Trivedi P, Leach JE, Tringe SG. et al. Plant-microbiome inter-actions: from community assembly to plant health. Nat Rev Microbiol. 2020; 18:607-21

[10]

Zipfel C. Early molecular events in PAMP-triggered immunity. Curr Opin Plant Biol. 2009; 12:414-20

[11]

Zipfel C. Pattern-recognition receptors in plant innate immunity. Curr Opin Immunol. 2008; 20:10-6

[12]

Chen T, Nomura K, Wang X. et al. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature. 2020; 580:653-7

[13]

Pfeilmeier S, Werz A, Ote M. et al. Leaf microbiome dysbiosis trig-gered by T2SS-dependent enzyme secretion from opportunistic Xanthomonas pathogens. Nat Microbiol. 2024; 9:136-49

[14]

Song S, Morales Moreira Z, Briggs AL. et al. PSKR1 balances the plant growth-defence trade-off in the rhizosphere microbiome. Nat Plants. 2023; 9:2071-84

[15]

Nations 2022. Nutrition: Food Loss and Waste. levels%20

[16]

( 15 September 2022)

[17]

Kader AA. Increasing food availability by reducing postharvest losses of fresh produce. Acta Hortic. 2005; 682:2169-76

[18]

Abdelfattah A, Wisniewski M, Droby S. et al. Spatial and com-positional variation in the fungal communities of organic and conventionally grown apple fruit at the consumer point-of-purchase. Hortic Res. 2016; 3:16047

[19]

Wassermann B, Muller H, Berg G. An apple a day: which bacteria do we eat with organic and conventional apples? Front Microbiol. 2019b; 10:1629

[20]

Abdelfattah A, Freilich S, Bartuv R. et al. Global analysis of the apple fruit microbiome: are all apples the same? Environ Microbiol. 2021; 23:6038-55

[21]

Abdelfattah A, Whitehead SR, Macarisin D. et al. Effect of wash-ing, waxing and low-temperature storage on the postharvest microbiome of apple. Microorganisms. 2020; 8:944

[22]

Liu J, Abdelfattah A, Norelli J. et al. Apple endophytic microbiota of different rootstock/scion combinations suggests a genotype-specific influence. Microbiome. 2018; 6:18

[23]

Bosch Y, Britt E, Perren S. et al. Dynamics of the apple fruit microbiome after harvest and implications for fruit quality. Microorganisms. 2021; 9:272

[24]

Van Keer C, Vanden Abeele P, Swings J. et al. Acetic acid bacteria as causal agents of browning and rot of apples and pears. Zen-tralblatt für Bakteriologie Mikrobiologie und Hygiene: I Abt Originale C: Allgemeine, angewandte und ökologische Mikrobiologie. 1981; 2: 197-204

[25]

Chinchilla D, Bauer Z, Regenass M. et al. The Arabidopsis recep-tor kinase FLS2 binds flg22 and determines the specificity of flagellin perception. Plant Cell. 2006; 18:465-76

[26]

Bai S, Dong C, Li B. et al. A PR-4 gene identified from Malus domes-tica is involved in the defense responses against Botryosphaeria dothidea. Plant Physiol Biochem. 2013; 62:23-32

[27]

Zhou Z, Zhu Y, Tian Y. et al. MdPR4, a pathogenesis-related protein in apple, is involved in chitin recognition and resistance response to apple replant disease pathogens. J Plant Physiol. 2021; 260:153390

[28]

Argenta LC, De Freitas ST, Mattheis JP. et al. Character-ization and quantification of postharvest losses of apple fruit stored under commercial conditions. HortScience. 2021; 56: 608-16

[29]

Spotts RA, Cervantes LA, Mielke MA. Variability in postharvest decay among apple cultivars. Plant Dis. 1999; 83:1051-4

[30]

Janisiewicz WJ. Control of storage decay of apples with Sporobolomyces roseus. Plant Dis. 1994; 78:466

[31]

Thomma BPHJ. Alternaria spp.: from general saprophyte to spe-cific parasite. Mol Plant Pathol. 2003a; 4:225-36

[32]

Ianiri G, Pinedo C, Fratianni A. et al. Patulin degradation by the biocontrol yeast Sporobolomyces sp. is an inducible process. Toxins (Basel). 2017; 9:61

[33]

Sanzani SM, Sgaramella M, Mosca S. et al. Control of Penicillium expansum by an epiphytic Basidiomycetous yeast. Horticulturae. 2021; 7:473

[34]

Domka A, Jedrzejczyk R, Wazny R. et al. Endophytic yeast protect plants against metal toxicity by inhibiting plant metal uptake through an ethylene-dependent mechanism. Plant Cell Environ. 2023; 46:268-87

[35]

Berrios L. The genus Caulobacter and its role in plant micro-biomes. World J Microbiol Biotechnol. 2022; 38:43

[36]

Estrada-De Los Santos P, Solano-Rodriguez R, Matsumura-Paz LT. a plant-associated species. Arch Microbiol. 2014; 196:811-7

[37]

Guro P, Ulianich P, Shaposhnikov A. et al. Draft genome sequence of the bacterium Cupriavidus sp. strain D39, inhabiting the rhizosphere of pea plants (Pisum sativum L.). Microbiol Resour Announc. 2023; 12:e0135422

[38]

Luo D, Langendries S, Mendez SG. et al. Plant growth promotion driven by a novel Caulobacter strain. Mol Plant-Microbe Interact. 2019; 32:1162-74

[39]

Preston GM. Plant perceptions of plant growth-promoting pseu-domonas. Philos Trans R Soc Lond Ser B Biol Sci. 2004; 359: 907-18

[40]

García-Seco D, Bonilla A, Algar E. et al. Enhanced blackberry production using Pseudomonas fluorescens as elicitor. Agron Sustain Dev. 2012; 33:385-92

[41]

Ares A, Pereira J, Garcia E. et al. The leaf bacterial microbiota of female and male kiwifruit plants in distinct seasons: assessing the impact of pseudomonas syringaepv. Actinidiae. Phytobiomes J. 2021; 5:275-87

[42]

Gomes RJ, Borges MF, Rosa MF. et al. Acetic acid bacteria in the food industry: systematics, characteristics and applications. Food Technol Biotechnol. 2018; 56:139-51

[43]

Gupta A, Singh VK, Qazi GN. et al. Gluconobacter oxydans: its biotechnological applications. J Mol Microbiol Biotechnol. 2001; 3: 445-56

[44]

Jousset A, Bienhold C, Chatzinotas A. et al. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J. 2017; 11:853-62

[45]

Shade A, McManus PS, Handelsman J. Unexpected diversity during community succession in the apple flower microbiome. MBio. 2013; 4:1-12

[46]

Nian L, Xie Y, Zhang H. et al. Vishniacozyma victoriae: an endo-phytic antagonist yeast of kiwifruit with biocontrol effect to Botrytis cinerea. Food Chem. 2023; 411:135442

[47]

Al Riachy R, Strub C, Durand N. et al. The influence of long-term storage on the epiphytic microbiome of postharvest apples and on Penicillium expansum occurrence and Patulin accumulation. Toxins. 2024; 16:102

[48]

Wang J, Wang R, Kang F. et al. Microbial diversity composition of apple tree roots and resistance of apple Valsa canker with different grafting rootstock types. BMC Microbiol. 2022; 22:148

[49]

Caporaso JG, Lauber CL, Walters WA. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA. 2011; 108:4516-22

[50]

Ghannoum MA, Jurevic RJ, Mukherjee PK. et al. Characterization of the oral fungal microbiome (mycobiome) in healthy individ-uals. PLoS Pathog. 2010; 6:e1000713

[51]

White JR, Maddox C, White O. et al. CloVR-ITS: automated inter-nal transcribed spacer amplicon sequence analysis pipeline for the characterization of fungal microbiota. Microbiome. 2013; 1:6

[52]

Lundberg DS, Yourstone S, Mieczkowski P. et al. Practical inno-vations for high-throughput amplicon sequencing. Nat Methods. 2013; 10:999-1002

[53]

Bolyen E, Rideout JR, Dillon MR. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019; 37:852-7

[54]

Rognes T, Flouri T, Nichols B. et al. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016; 4:e2584

[55]

Yilmaz P, Parfrey LW, Yarza P. et al. The SILVA and "all-species living tree project (LTP)" taxonomic frameworks. Nucleic Acids Res. 2014;42:D643-8

[56]

Abarenkov K, Zirk A, Piirmann T. et al. UNITE General FASTA Release for Fungi. COMMUNITY, U ed. Version 04.02.2020. UNITE Community. https://doi.org/10.15156/BIO/786368

[57]

Liber JA, Bonito G, Benucci GMN. CONSTAX2: improved taxo-nomic classification of environmental DNA markers. Bioinfor-matics. 2021; 37:3941-3

[58]

McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013; 8:e61217

[59]

Oksanen J, Guillaume Blanchet F, Kindt R. et al. Community Ecology Package. 2.6- 4 ed. 2022. https://cran.r-project.org/web/packages/vegan/index.html

[60]

Wickham H. ggplot2: Elegant Graphics for Data Analysis. Cham: Springer International Publishing; 2016

[61]

Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15:550

[62]

McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 2014; 10: e1003531

[63]

Kolde R. 2022. Pretty Heatmaps. 1.0.12 ed. https://cran.r-project. org/web/packages/pheatmap/pheatmap.pdf

[64]

De Cáceres M, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009; 90: 3566-74

[65]

Tackmann J, Matias Rodrigues JF, Von Mering C. Rapid infer-ence of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data. Cell Systems. 2019; 9:286-296.e8

[66]

Bezanson J, Edelman A, Karpinski S. et al. Julia: a fresh approach to numerical computing. SIAM Rev. 2017; 59:65-98

[67]

Shannon P, Markiel A, Ozier O. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13:2498-504

[68]

Wiese R, Eiglsperger M, Kaufmann M. yFiles—Visualization and Automatic Layout of Graphs. In: Jünger M, Mutzel P, eds. Graph Drawing Software. Springer Berlin Heidelberg: Berlin, Heidelberg, 2004

[69]

Assenov Y, Ramírez F, Schelhorn S-E. et al. Computing topolog-ical parameters of biological networks. Bioinformatics. 2008; 24: 282-4

[70]

Morris JH, Apeltsin L, Newman AM. et al. clusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC Bioinformatics. 2011; 12:436

[71]

Utriainen M, Morris JH. clusterMaker2: a major update to clus-terMaker, a multi-algorithm clustering app for Cytoscape. BMC Bioinformatics. 2023; 24:134

[72]

Zafeiropoulos H, Michail Delopoulos EI, Erega A. et al. Micro-betag: simplifying microbial network interpretation through annotation, enrichment tests and metabolic complementarity analysis. 2024; bioRxiv, 2024.10.01.616208

[73]

Gu Z, Gu L, Eils R. et al. Circlize implements and enhances circular visualization in R. Bioinformatics. 2014; 30:2811-2

[74]

RCORETEAM. R: A Language and Environment for Statistical Com-puting. R Foundation for Statistical Computing; 2022. https://www.R-project.org

[75]

Graves S, Piepho H,Selzer L. _multcompView: visualizations of paired Comparisons_. 2024

[76]

Wickham H, Francois R, Henry L. et al. Dplyr: agrammarofdata manipulation. 2023; R package version 1.1.4

[77]

Wickham H, Vaughan D, Girlich M. Tidyr: tidy messy data. 2024; R package version 1.3.1

[78]

Schauberger P, Walker A. Openxlsx: read, write, and edit xlsx files. 2023; R package version 4.2.5.2

[79]

Wickham H, Averick M, Bryan J. et al. Welcome to the tidyverse. J Open Source Softw. 2019; 4:1686

[80]

Wickham H. Stringr: simple, consistent wrappers for common string operations. 2023; R package version 1.5.1

[81]

Slowikowski K.Ggrepel: automatically position non-overlapping text labels with ‘ggplot2’. 2024; R package version 0.9.5

[82]

Neuwirth E. RColorBrewer: ColorBrewer palettes. 2022; R pack-age version 1.1-3

[83]

Krens FA, Schaart JG, Groenwold R. et al. Performance and long-term stability of the barley hordothionin gene in multiple trans-genic apple lines. Transgenic Res. 2011; 20:1113-23

[84]

Vergne E, Dugé De Bernonville T, Dupuis F. et al. Membrane-targeted HrpNEa can modulate apple defense gene expression. Mol Plant-Microbe Interact. 2014; 27:125-35

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